Information fusion with Bayes networks in computer-aided detection systems

ABSTRACT

This invention provides an information fusion method for multiple indicators of cancers detected with multiple channels. Each channel consists of specifically tuned detectors, features, classifiers and Bayes networks. The outputs of the Bayes networks are probabilities of malignancy for the detections passing the corresponding classifier.

CROSS REFERENCE TO RELATED APPLICATION

This invention claims benefit of U.S. Provisional filing, Ser. No.60/333,825, filed Nov. 20, 2001.

BACKGROUND OF THE INVENTION

1. Field of the Invention

This invention pertains to methods of combining multiple types ofinformation in computer-aided detection (CAD) systems.

2. Description of the Prior Art

Mammographic CAD systems have existed in research environments since the1960's. Currently available systems find indicators of cancer inmammograms. Typical indicators are clusters of microcalcifications,masses, and spiculated masses. Each indicator may be detected with aspecific processing channel. Each channel provides a set of detectedregions considered as potentially cancerous. Various methods have beendescribed for combining sets of detections from multiple channels.

The issue of combining multiple detectors in a CAD system has beenaddressed in prior US patents. Rogers et al, in U.S. application Ser.No. 09/602,762 describe a method for combining outputs ofmicrocalcification and density channels. Case Based Ranking (CBR) limitsthe total number of marks in a case by retaining only the “best” subset.Additional limits are applied to the total number of marks per image andthe total number of each type of mark allowed on any one image of thecase. In application Ser. No. 09/602,762, the ranking is based on adifference of discriminants score, which is proportional to theprobability of cancer.

Roehrig et al, in U.S. Pat. No. 6,198,838 describe a method forcombining outputs of two independent channels: mass detector andspiculation detector. Each detector processes the image, detects regionsof interest (ROIs), and computes sets of mass and spiculation featurevectors characterizing the ROI. The combining method taught by Roehrigconsists of concatenating mass and spiculation information into afeature vector, then applying it to a classifier. ROIs passing theclassifier are then displayed as final detections.

In Roehrig et al, the simple concatenation of features from distinctchannels has one very undesirable effect. The probability distributionsof the cancer/not cancer concatenated feature are more confusable thaneither of the original feature vectors. This is because both spiculatedand non-spiculated lesions are represented by a single feature vectorconsisting of mass and spiculation elements. The features specific forspiculatedness will be “noiselike” for regions of interest containingmasses. Similarly, the massness features will be noiselike for regionscontaining spiculations. Assuming an equal number of massness andspiculatedness features, half of the features for any lesion will benoise. The end result is a reduction in classifier accuracy in a fieldedsystem.

Viewed from a different perspective, the classifier is forced toconsider masses and spiculated masses as a single “cancer” category.Since feature vectors derived from independent and possibly orthogonalsources, the values of feature vectors in the “cancer” category aredispersed over a larger volume of feature space than would be requiredif the separate classifiers were applied for the mass and spiculatedmass channels.

To achieve higher classification accuracy in CAD systems, there isclearly a need for an improved method to combine lesion information.

SUMMARY OF THE INVENTION

The present invention provides a means for computing a probability ofmalignancy for a lesion by combining multiple types of lesioninformation and, in particular, provides a system and method forcombining information obtained from detections relating to differentlesion-types to provide an output comprising a final display ofsuspicious lesions. Plural channels are provided for detecting andprocessing information specific to each lesion-type and, in thepreferred embodiment, the channels correspond to calcifications, massesand spiculations. Each channel includes a tuned detector, classifier,and Bayes network wherein the outputs of the Bayes networks areprobabilities of malignancy. The malignancies are rank ordered in a rulebased post-processing operation to provide the final display ofsuspicious lesions.

According to one aspect of the invention, a method of detectingmalignancy of a lesion is provided, comprising the steps of: computingevidence from each lesion, providing the evidence to a Bayes network,and computing a probability of malignancy for each lesion.

According to another aspect of the invention, a method for use in a CADsystem for providing detections of suspicious lesions is provided,wherein the method provides for selection of lesions for final displayand comprises the steps of: calculating a probability of malignancy foreach lesion, and using the probability of malignancy as a selectioncriteria affecting a final display of suspicious lesions.

According to yet another aspect of the invention, a method of detectingmalignancy of a lesion is provided, comprising the steps of: usingindependent lesion-type specific detectors to detect lesions, computinglesion-type specific evidence corresponding to each detected lesion,providing the lesion-type specific evidence to lesion-type specificBayes networks, and computing a lesion-type specific probability valuerelating to malignancy for at least some of the detected lesions.

According to a further aspect of the invention, A method of enhancingimage regions corresponding to spiculations comprising: providing a setof line kernels and a set of spiculation kernels, filtering a medicalimage with the set of line kernels to create a binary line responseimage and an angle orientation image, removing spurious lines from theline response image producing a pruned line image, separating lines inthe pruned line image according to directions specified by the angleorientation image to produce a set of orientation specific line images,multiplying the orientation specific line images with the medical imageto produce a set of intensity-weighted line images, filtering theintensity-weighted line images with the set of spiculation kernels toproduce a set of spiculation response images, and storing the maximumspiculation response value at each pixel location, producing an imagewith enhanced spiculations.

According to another aspect of the invention, a method for detectingspiculations in a medical image is provided, comprising: providing a setof line kernels and a set of spiculation kernels, filtering a medicalimage with the set of line kernels to create a binary line responseimage and an angle orientation image, removing spurious lines from theline response image producing a pruned line image, separating lines inthe pruned line image according to directions specified by the angleorientation image to produce a set of orientation specific line images,multiplying the orientation specific line images with the medical imageto produce a set of intensity-weighted line images, filtering theintensity-weighted line images with the set of spiculation kernels toproduce a set of spiculation response images, storing the maximumspiculation response value at each pixel location, producing aspiculation image, and thresholding the spiculation image to produceimage regions containing spiculations.

Other objects and advantages of the invention will be apparent from thefollowing description, the accompanying drawings and the appendedclaims.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is an overview of the invention.

FIG. 2 shows a CAD system with independent channels, and informationfusion by Bayes Networks and Case Based Ranking.

FIG. 3 shows a system to detect spiculations.

FIG. 4 is a block diagram of a line detection method.

FIG. 5 illustrates line kernels.

FIG. 6 is a block diagram for the method of generating orientationspecific line images.

FIG. 7 shows the method for removing spurious lines.

FIG. 8 is a block diagram for the method of creating a spiculationimage.

FIG. 9 illustrates spiculation kernels.

FIG. 10 is a block diagram for the spiculated region detector.

FIG. 11 illustrates the architecture of the CAD system with BayesNetworks incorporated into each channel.

FIG. 12 shows Bayes network topologies for the Calc, Mass, andSpiculation channels.

FIG. 13 shows means and variances for Calc, Mass, and spiculationdiscriminant scores.

FIG. 14 shows conditional probability tables of collocation for theCalc, Mass, and Spiculation channels.

FIG. 15 is a flow chart for the Case Based Ranking method.

DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENT

The primary advantage of this invention is obtained by providing aninformation fusion method for multiple indicators of cancers detectedwith multiple channels. Each channel is lesion type specific andcomprises specifically tuned detectors, features, classifiers and Bayesnetworks. The outputs of the Bayes networks are probabilities ofmalignancy for the detections passing the corresponding classifier.These probabilities are rank ordered in a rule based post-processingstep to provide final CAD system outputs.

FIG. 1 shows an overview of the system. Mammograms, 1000, are obtainedduring a radiologic exam. The output of the exam may be either a set offilms or a set of digital images depending on the type of equipment usedduring the exam. When the mammograms are films, the CAD system, 2000,uses a film digitizer to generate digital images of each film forsubsequent analysis. When the mammograms are created by a full fielddigital mammography (FFDM) system, the digitization step is not requiredand the CAD system operates directly on the digital images. Themammograms are processed by the CAD system, producing a set of outputdetections and associated attributes, 3000. In the prior art, as inRoehrig, such an output provides final detections. In this invention,additional attributes are associated with each detection which are thenused to generate fusion information, 4000. The fusion information isinput to a Bayesian Information Fusion step, 5000. In this step,probabilities of cancer, 6000, are computed for each detection. We willshow that the union of the Generate Fusion Input Information andBayesian Information Fusion steps, 4000 and 5000, with the preceding andsubsequent steps is an important aspect of this invention. Thedetections are ranked by probabilities and cancer categories in the CaseBased Ranking step, 7000. This step limits the number of detections to amaximum number per category per case, producing the final set ofdetections, 8000. Each element is now described in more detail.

The invention applied to a CAD system is shown in FIG. 2. CAD systemstypically provide separate channels for each category of cancer to bedetected, as shown in the dashed box, 2000. Here we show a three channelsystem. The left channel detects and classifies microcalcificationregions of interest (ROIs). The center channel detects and classifiesmass ROIs. The right channel detects and classifies spiculation ROIs.The first two steps in each channel are to detect potential lesions andcompute features for each ROI. The third step provides ROIclassification within each channel. The classifiers output difference ofdiscriminant scores for each ROI. Prior to computing contextinformation, 4000, the number of detections per image is limited to apredetermined maximum value per category, not shown. This limitingimproves the quality of subsequent context information. In a preferredembodiment, the microcalcification channel is limited to two detectionsper image, while the mass and spiculation channels are limited to threedetections per image. The discriminant scores and other informationcomprise evidence input to corresponding Bayes networks, 5000, as thefinal step within each channel. The Bayes networks compute theprobability of cancer for each ROI. The probability scores are passed tothe Case Based Ranking step, 7000. Here, ROIs are rank ordered and rulesapplied for restricting the number of detections per case for differentindicators of cancer. The output of post processing is a final set ofROIs, 8000. The final detections are displayed to assist a radiologistin the detection of cancer.

The microcalcification and mass channels, the left and center channelsin FIG. 2, have been described in U.S. application Ser. No. 09/602,762,filed Jun. 23, 2000, which application is assigned to the assignee ofthe present application and is incorporated herein by reference.Although context fusion is general and may be applied to a system withonly two channels, such as mass and microcalcification, a preferredembodiment of the present application additionally includes aspiculation channel as the third indicator of cancer which is nowdescribed in further detail.

Spiculation Channel

An overview of the spiculation detector is shown in FIG. 3.Preprocessing, not shown here, is applied to the set of input images asdescribed in U.S. application Ser. No. 09/602,762. The spiculationchannel operates on images with approximately 175 micron pixel spacing.Line kernels of eight different orientations, 2310, are convolved withthe input image, 1001, in step 2300. Lines are detected in the step2400. Detected line images are formed at each of the eight orientations.The detected line images are then weighted by the original imageintensity in step 2500.

Weighting by original image intensity reduces the contribution of lineswith low intensity. Using the raw intensity at any stage beyond theinitial line detection is considered undesirable when striving for arobust system design. However, if the spiculation system had to relysolely on binary line images to detect spiculation it would result inmany false positives detections in dim, low contrast regions. Toeliminate this problem the line images are weighted by the originalimage intensity. In so doing, a variable level of importance isestablished to the line structure. The importance is directly related tothe intensity of the detected lines.

After weighting by original image intensity, the detected line imagesare then subsampled by a factor of four in each dimension, step 2600. Aset of spiculation kernels, 2710, which detect regions of diametricallyopposed radiating lines, are convolved with the subsampled line imagesin step 2700. Regions with large responses to the spiculation kernelsare detected in step 2800, producing a set of spiculation ROIs, 2900.Each step in the spiculation detector is now described in more detail.

Line Detection

An overview of the line detection algorithm is shown in FIG. 4. First,eight line kernels are convolved with the input image. The kernelorientations span 180 degrees in 22.5 degree steps. Each kernel consistsof a single pixel width line at eight different orientations in a 9 by 9pixel window, normalized to zero mean. The line kernels are shown inFIG. 5.

Referring again to FIG. 4, convolving an input image, 1001, with the setof line kernels, 2401 through 2408, produces a set of eight filteredimages, 2411 through 2418. Let the n^(th) filtered image be denoted byI_(n)(x,y). Then, the line response image, L(x,y), and angle responseimage, A(x,y) are formed as:L(x,y)=max_(n) {I _(n)(x,y)}; n=1, . . . , 8  (1)A(x,y)=argmax_(n) {I _(n)(x,y)}; n=1, . . . , 8  (2)

Equation 1 shows the value of the line response image at location (x,y)is selected as the maximum response across the eight filtered images atthat location. Equation 2 shows the value of the angle response image atlocation (x,y) is the index of the filtered image that provided themaximum response.

In step 2420 the maximum values from the set of filtered images at each(x,y) location are stored to form the line response image, 2430. In step2440, the index of the filtered image providing the maximal response isstored, producing the angle orientation image, 2450.

A local constant false alarm rate (CFAR) thresholding method is appliedto detect lines from the line response image, as shown in FIG. 6. Thelocal mean and standard deviation of the line response image, 2430, arecomputed in a 21 by 21 pixel neighborhood centered at pixel location(x,y) in step 2452. In step 2454, the pixel under test, L(x,y), iscompared to a local threshold, in a preferred embodiment computed as thelocal mean plus 0.5 times the local standard deviation. If L(x,y) isgreater than the threshold, location (x,y) of the local thresholded lineimage, 2456, is set to ‘1’, otherwise, location (x,y) is set to ‘0’.

The local threshold line image may have unwanted lines detected at areasin the original image with high intensity or contrast, such as along thechest wall or the skin-air line. These types of spurious lines areremoved in step, 2460, shown in FIG. 7.

The line removal algorithm requires as inputs the breast mask, computedin a pre-processing step, the line response image, 2430, and the localthreshold line image, 2456. The breast mask, a binary image with valuesof ‘1’ corresponding to regions of breast tissue and ‘0’ elsewhere, iseroded by N=31 pixels, in step 2642. Erosion produces a smaller, erodedbreast mask, 2464, free from the artifacts that we are trying to remove.The global mean and standard deviation in the line response image iscalculated from the area of the eroded breast mask in step 2465. Thespurious lines are then detected and removed by determining the linesthat are statistical outliers. A global threshold is computed, in apreferred embodiment, asT=global mean+3.5*global standard deviationand applied in step 2466. For pixels (x,y) in the line response imagegreater than the threshold T, the remove line mask image, 2467, atlocation (x,y) equals ‘1’. Otherwise location (x,y) of the remove linemask image equals ‘0’. In step 2468, the remove line image mask isapplied to the local threshold line image, 2456. Pixels in the localthreshold line image are cleared, that is set to ‘0’, at pixel locationsfor which the remove line mask image equals ‘1’. This step effectivelyremoves spurious lines detected due to the chest wall and the skin-airline, producing a pruned line image, 2470.

Referring again, to FIG. 6, the final step of the line detection methodis to separate the lines by orientation, 2480. This is accomplished byassociating each ‘on’ pixel in the pruned line image 2470 to anorientation using the angle orientation image, 2450. This step produceseight orientation specific line images, 2490, for subsequent use in thespiculation detector.

Spiculation Detection

A block diagram of the spiculation detection method is shown in FIG. 8.

The role of the spiculation detector is to identify regions exhibitingdiametrically opposed radiating line structure. The inputs to thespiculation detection method are the set of spiculation kernels, 2710and the set of orientation specific line images, 2490. In a preferredembodiment, there are four sizes of spiculation kernels oriented ateight angles.

Each orientation specific line image is convolved with an associatedorientation specific spiculation kernel of specified edge length in step2700. The response is accumulated across the range of orientations, step2810. In this embodiment, four spiculation response images are created,one for each speculation kernel size. The set of spiculation responseimages are shown in box 2815. In step 2820, a spiculation image, 2825,is created by keeping the maximum response from the set of spiculationresponse images at each (x,y) pixel location. In step 2830, the index ofthe spiculation kernel providing the maximum response of step 2820 isstored, producing the winning kernel image, 2835.

Referring to FIG. 9, each spiculation kernel consists of a disc withwedges at a specific orientation, corresponding to a line kernelorientation. In the hashed regions outside the disc, the kernel has zerocontribution, the white wedges denote a constant positive contribution,and the wavy wedges denote a constant negative contribution. Each of thekernels are zero mean. The positive wedges span 45 degrees, and areoriented from 0 to 180 degrees in steps of 22.5 degrees. To detect arange of sizes, four sizes of spiculation kernels are used. Thespiculation kernels are square windows with edge lengths, L, of 15, 25,35, and 45 pixels.

Given the spiculation image 2825, detections must be created. A blockdiagram of the method is given in FIG. 10. The region of interest (ROI)detector for the spiculation system is a global CFAR. The global mean,Gm, and standard deviation Gsd, are calculated over the breast tissuearea of the spiculation response image in step 2840. If the spiculationresponse for a given pixel is greater than Gm+n1*Gsd, then the pixel isretained as part of an ROI. In a preferred embodiment, n1=4.5. Connectedcomponent analysis, step 2850, groups pixels satisfying the thresholdinto a set of distinct objects where an object is a set of adjacentpixels (4 or 8 connected), producing a set of binary detection masks,2855. The binary detection masks indicate the location and extent ofdetections in the spiculation image. The detection masks are rectangularand bound each object from the connected component analysis. Pixels inthe mask corresponding to image locations exceeding the threshold ofstep 2845 are set to ‘1’.

The spiculation image is re-thresholded in step 2860. A second thresholdis computed as Gm+n2*Gsd with n2=3.0. Connected component analysis ofthe re-thresholded image provides a second set of detections. Only thosere-thresholded detections containing an object from the firstthresholding are retained.

The outputs are bounding boxes, 2870, and detection maps, 2880, for there-thresholded objects. A detection map is a binary mask with ‘1’sindicating pixel locations corresponding to the re-thresholded object.The bounding box is specified as the row and column coordinates of theupper left and lower right corners of a rectangle sized to circumscribethe object.

Bayes Network

FIG. 11 shows how Bayes networks are used in this invention. Theindividual channels, 2010, 2020, and 2030, detect specific types oflesions. Features are computed and applied to corresponding classifiers,producing discriminant scores. The discriminant scores and otherinformation are used as evidence, 4010, 4020, and 4030, for the Bayesnetworks, 5100, 5200, and 5300, within each channel. The Bayes networkscompute probabilities of cancer given the evidence, 6100, 6200, and6300. These probabilities are provided to a Case Based Ranking step,7000, to produce final detections, 8000.

Bayes Network Evidence

Many different possible types of information are potentially useful asevidence to Bayes networks. In a preferred embodiment, shown in FIG. 11,steps 4010, 4020, and 4030 show two basic types of evidence: normalizeddifference of discriminants, and collocation indicators. Each of theseare now described. Normalized differences of discriminants are computedas follows. First, classifiers are trained in each channel as describedin application Ser. No. 09/602,762, producing two discriminant functionsand a threshold value for each indicator of cancer. Within each channel,the true positive (TP) discriminant corresponds to the classifierrepresenting that channel's category and the false positive (FP)discriminant to the non-TP categories. Discriminant scores areequivalent to distances from prototypical examples. For example, if inthe calc channel a detection produces a small TP discriminant and alarge FP discriminant, the detection is more likely to be a calc thannot. Within each channel, the normalized difference is computed by firstsubtracting the TP from the FP discriminant. Then, the threshold valueis subtracted from the difference of discriminants score and the resultraised to a power transform value. In the calc and mass channel, thepower transform value equals 0.35; in the spiculation channel, the powertransform value is 0.25.

Collocation indicators are binary units of evidence. A collocationindicator is set to ‘1’ if a detection in one channel overlaps adetection in another channel.

The specific evidence used in the Bayes networks of each channel is nowdescribed.

Calc Bayes Network

In the calc channel, the normalized difference of discriminants and acollocation indicator are input to the Calc Bayes network. Thecollocation indicator is ‘1’ for calc detections overlapping either amass or spiculation detection. The topology of the Mass Bayes Network isshown in FIG. 12( a).

Mass Bayes Network

In the mass channel, the normalized difference of discriminants and twocollocation indicators are input to the Mass Bayes network. The firstcollocation indicator is ‘1’ for mass detections overlapping amicrocalcification detection. The second collocation indicator is ‘1’for mass detections overlapping a spiculation detection. The topology ofthe Mass Bayes Network is shown in FIG. 12( b).

Spiculation Bayes Network

In the spiculation channel, the normalized difference of discriminantsand two collocation indicators are input to the Spiculation Bayesnetwork. The first collocation indicator is ‘1’ for spiculationdetections overlapping a microcalcification detection. The secondcollocation indicator is ‘1’ for spiculation detections overlapping amass detection. The topology of the Spiculation Bayes Network is shownin FIG. 12( c).

Calculating Probabilities

The evidence for each ROI is supplied to the Bayes Network, and theprobability of cancer is calculated for each ROI. The probabilitycalculation requires conditional probability distributions or functionsto be estimated from a set of training data. That is, the evidence istabulated for detections of known “cancer” or “not cancer” categories.In a preferred embodiment, let the conditional distributions resultingfrom training data be as shown in FIGS. 13 and 14. The conditionaldistributions of the normalized difference of discriminants is assumedto be Gaussian, and therefore specified by mean and variance. FIG. 13shows the mean and standard variance values for the three channels. Theremaining evidence is discrete, and represented in conditionalprobability tables. FIG. 14 shows the conditional probability tables forthe collocation evidence. Collocation is defined as the centroid of thefirst detection type being located inside the second detection type.

When computing the probability of cancer for a lesion in an input image,the lesion's particular values of evidence are used to obtainconditional probability values from FIGS. 13 and 14. The probabilitiesof not cancer and cancer (i=0, 1) after evidence is considered,P(C_(i)|e₁, e₂, . . . , e_(n)), is given by

$\begin{matrix}{{\Pr\left( {\left. C_{i} \middle| e_{1} \right.,e_{2},\ldots\mspace{14mu},e_{n}} \right)} = \frac{{p\left( e_{1} \middle| C_{i} \right)}{p\left( e_{2} \middle| C_{i} \right)}\mspace{14mu}\ldots\mspace{14mu}{p\left( e_{n} \middle| C_{i} \right)}{\Pr\left( C_{i} \right)}}{\sum\limits_{j = 0}^{1}{{p\left( e_{1} \middle| C_{j} \right)}{p\left( e_{2} \middle| C_{j} \right)}\mspace{14mu}\ldots\mspace{14mu}{p\left( e_{n} \middle| C_{j} \right)}{\Pr\left( C_{j} \right)}}}} & (3)\end{matrix}$where p(e_(k)|C_(i)) is the conditional probability value of the k^(th)element of evidence given the i^(th) cancer category and is obtainedfrom conditional distribution tables. The calculations will bedemonstrated for the calc Bayes network. The mass and spiculationcalculations are accomplished in the same fashion.

Example Computations for the Calc Bayes Network

The Calc Bayes Network, 5100, computes the probabilities of “not cancer”and “cancer” given Calc Evidence, 6100. These probabilities are computedas

$\begin{matrix}{{\Pr\left( {\left. C_{i} \middle| e_{1} \right.,e_{2}} \right)} = \frac{{p\left( e_{1} \middle| C_{i} \right)}{p\left( e_{2} \middle| C_{i} \right)}{\Pr\left( C_{i} \right)}}{\sum\limits_{j = 0}^{1}{{p\left( e_{1} \middle| C_{j} \right)}{p\left( e_{2} \middle| C_{j} \right)}{\Pr\left( C_{j} \right)}}}} & (4)\end{matrix}$where Pr(C₀) and Pr(C₁) are probabilities of “not cancer” and “cancer”,assumed here to both equal 0.5. Assume the evidence for a calc detectionare as follows: the calc detection is not collocated with a mass orspiculation detection and the calc classifier produces a discriminantvalue of 1.75. Thus, the first evidence value is ‘0’, and theconditional probabilities of “not cancer” and “cancer” are taken fromTable 14(c) as p(e₁=0|C₀)=0.0948 and p(e₁=0|C₁)=0.1662. To obtainp(e₂=1.75|C₀), use the means and variances for the calc discriminant inTable 13(a). Evaluating the Gaussian probability density function withmean of 1.4661 and variance of 0.2282 at 1.75 gives

${p\left( {e_{2} = \left. 1.75 \middle| C_{0} \right.} \right)} = {{\frac{1}{\sqrt{2\;{\pi(0.2282)}}}{\exp\left( {\frac{- 1}{2}\frac{\left( {1.75 - 1.4661} \right)^{2}}{0.2282}} \right)}} = 0.6999}$andp(e ₂=1.75|C ₁)=0.5219.

Using the values above, the conditional probabilities of “not cancer”and “cancer” given the particular evidence are computed according toEquation 4:

${\Pr\left( {{\left. C_{0} \middle| e_{1} \right. = 0},{e_{2} = 1.75}} \right)} = {\frac{0.0948 \cdot 0.6999 \cdot 0.5}{{0.0948 \cdot 0.6999 \cdot 0.5} + {0.1662 \cdot 0.5219 \cdot 0.5}} = 0.8844}$${\Pr\left( {{\left. C_{1} \middle| e_{1} \right. = 0},{e_{2} = 1.75}} \right)} = {\frac{0.1662 \cdot 0.5219 \cdot 0.5}{{0.0948 \cdot 0.6999 \cdot 0.5} + {0.1662 \cdot 0.5219 \cdot 0.5}} = 0.1156}$

The calculations of both “not cancer” and “cancer” probabilities areshown here for completeness. However, only the probability of cancer isused in subsequent processing. Mass and spiculation probabilities arecomputed in the same fashion. The Bayes probabilities for each lesionfrom each channel are provided to the Case Based Ranking step, 7000.

Case Based Ranking

This section describes the context processing flow, ROI probabilitythresholding and case-based rank ordering. FIG. 15 shows an overallblock diagram of the post-processing system. The processing is performedon each image of the case and surviving ROIs accumulate across theimages in the case. Steps above the Image/Case line are performed oneach image. These steps provide a set of case values for processingbelow the Image/Case line. The operation of the context and caseprocessing section is now described.

Once the probability of cancer is calculated using the Bayesian network,the detections within an image are analyzed separately for eachdetection type. First, the probability of cancer for each ROI iscompared to a predetermined channel-specific threshold. All ROIs with“cancer” probabilities less than the threshold are removed. ROIs with“cancer” probabilities greater than a threshold are retained forsubsequent processing. In a preferred embodiment, the thresholds are asshown in Table 1.

TABLE 1 Bayes Thresholds. Channel Threshold Calc 0.3 Mass 0.29 Spic 0.44

In the Mass and Spiculation channels, detections with probabilitiesgreater than corresponding thresholds are retained. If the detectionsare collocated, the detection with the greater Bayes probability isretained. Otherwise, both are retained. Collocation is determined bycomputing the centroid of both ROIs. If the centroid of a first ROI iswithin the second ROI, collocation is declared. After this point, massand spiculation ROIs are considered to be one type of detection,referred to as a “density” detection.

All remaining ROIs are then jointly ranked by Bayes probabilities,largest to smallest, across the case in step 7100. If more than apredetermined maximum number, C_(R), of ROIs remain, only the firstC_(R) ranked ROIs are retained, step 7200. Additional restrictions arethen applied to the number of calc and density ROIs in the case. Onlythe C_(C) most probable calc detections are retained in step 7300.Similarly, only the C_(D) most probable density ROIs are retained instep 7400. Recall, density ROIs are obtained from the combined list ofmass and spiculation ROIs. The surviving ROIs are the final detections,and are displayed in step 8000.

In a preferred embodiment, the maximum rank values are as shown in Table2. The values in Table 2 are based on four image cases. If the actualnumber of images in a case does not equal four, rank limits may berecomputed.

TABLE 2 Maximum rank values in four image cases. Case Rank Case RankParameter Limit Case 10 Calc 10 Mass  5

In a preferred embodiment, when only one image is in the case, limit thetotal number of marks to two; the calc and mass case rank limits alsoequal two. When the case consists of 2, 3, or four images, the ranklimits of Table 2 apply. When the case consists of more than fourimages, the rank limits are recomputed as:C _(R)=round(10*NumImages/4)C _(C)=round(10*NumImages/4)C _(M)=round(5*NumImages/4)where NumImages is the number of images in a case, and round( ) denotesrounding to the nearest integer.

The final marks that may appear on an output are those passing both theprobability thresholding and the case rank ordering from the contextimages.

Although the present invention has been described in terms of specificembodiments which are set forth in detail, it should be understood thatthis is by illustration only and that the present invention is notnecessarily limited thereto, since alternative embodiments not describedin detail herein will become apparent to those skilled in the art inview of the above description, the attached drawings and the appendedclaims. Accordingly, modifications are contemplated which can be madewithout departing from either the spirit or the scope of the presentinvention.

1. A method of detecting malignancy of a lesion, comprising the stepsof: computing evidence from each lesion based on specific type of saidlesion using multiple lesion-specific channels, wherein each channelcomprises a lesion-type specific tuned detector, lesion-type specificfeature and lesion-type specific classifier; providing said evidence toa lesion-type specific Bayes network; and computing a probability ofmalignancy for each lesion.
 2. The method of claim 1 wherein saidevidence comprises collocation to detected lesions, a ranking of eachlesion relative to other lesions in an image, and a discriminant scoreassociated with the lesion.
 3. The method of claim 1 wherein each ofsaid probabilities is compared to a threshold.
 4. The method of claim 3including performing a case-based-ranking, based on the probabilityvalue for each lesion, on all lesions passing the threshold, wherein apredetermined number of lesions having the highest probability value areretained.
 5. The method of claim 1 wherein said lesions comprisespecific lesion types, said step of computing evidence comprisescomputing lesion-type specific evidence, and said lesion-type specificevidence is provided to lesion-type specific Bayes networks forcomputing lesion-type specific probabilities.
 6. The method of claim 5wherein each of said probabilities is compared to a lesion-type specificthreshold.
 7. The method of claim 6 including determining whether twolesions of different lesion-types are collocated and, if collocated,selecting the lesion of the collocated lesions having the greaterprobability value.
 8. The method of claim 7 wherein said two lesions ofdifferent lesion-types comprise a mass lesion and a spiculation lesion.9. The method of claim 8 including performing a case-based-ranking,based on the probability value for each lesion, on all of the lesionspassing the lesion-type specific thresholds, wherein a predeterminednumber of lesions having the highest probability value are retained. 10.The method of claim 9 including limiting the number of calcificationlesions retained to a first predetermined number, limiting the combinednumber of mass lesions and spiculation lesions retained to a secondpredetermined number, and displaying the remaining retained lesions. 11.In a CAD system for detecting suspicious lesions, a method of selectinglesions for final display comprising the steps of: calculating aprobability of malignancy for each lesion based on specific type of saidlesion using multiple lesion-specific channels, wherein each channelcomprises a lesion-type specific tuned detector, lesion-type specificfeature, lesion-type specific classifier and lesion-type specific Bayesnetwork; and using the probability of malignancy as a selection criteriaaffecting a final display of suspicious lesions.
 12. The method of claim11 wherein the probability of malignancy is computed based on evidencederived from each lesion and provided to a Bayes network.
 13. The methodof claim 12 wherein said evidence comprises collocation of detectedlesions, a ranking of each lesion relative to other lesions in an image,and a discriminant score associated with the lesion.
 14. The method ofclaim 11 wherein the lesions are rank-ordered by probability ofmalignancy, and a predetermined maximum number of lesions having thehighest probability value, with reference to all lesions in a case, areretained.
 15. The method of claim 14 wherein a subset of thepredetermined maximum number of lesions of a specific lesion type, isretained.
 16. The method of claim 11 wherein the lesions arerank-ordered by probability of malignancy, and a predetermined maximumnumber of lesions having the highest probability value, with referenceto all lesions in an image, are retained.
 17. The method of claim 16wherein a subset of the predetermined maximum number of lesions of aspecific lesion type, is retained.
 18. A method of detecting malignancyof a lesion, comprising the steps of: using multiple independentlesion-type specific detectors to detect lesions, lesion-type specificfeatures and lesion-type specific classifiers to detect lesions;computing lesion-type specific evidence corresponding to each detectedlesion; providing said lesion-type specific evidence to lesion-typespecific Bayes networks; and computing a lesion-type specificprobability value relating to malignancy for at least some of saiddetected lesions.
 19. The method of claim 18 wherein the step ofcomputing a lesion-type specific probability value for at least some ofsaid detected lesions comprises computing said probability value foreach lesion of a particular lesion-type independently of lesion-typespecific evidence associated with lesions of a different lesion-type.20. The method of claim 18 wherein said lesion-type specific Bayesnetworks independently compute probability values for calcification,mass and spiculation lesions.